A Hybrid of Grey Wolf Optimization and Genetic Algorithm for Optimization of Hybrid Wind and Solar Renewable Energy System

doi:10.1007/s40305-021-00341-0

Journal of the Operations Research Society of China ›› 2022, Vol. 10 ›› Issue (4): 749-762.doi: 10.1007/s40305-021-00341-0

收稿日期:2019-05-21 修回日期:2020-11-27 出版日期:2022-12-30 发布日期:2022-11-09
通讯作者: Mukhdeep Singh Manshahia,Diriba Kajela Geleta E-mail:mukhdeep@gmail.com;kajeladiriba@yahoo.com

A Hybrid of Grey Wolf Optimization and Genetic Algorithm for Optimization of Hybrid Wind and Solar Renewable Energy System

Diriba Kajela Geleta^1,2, Mukhdeep Singh Manshahia¹

1 Department of Mathematics, Punjabi University, Patiala, Punjab 147002, India;
2 Department of Mathematics, Madda Walabu University, Bale Robe 4540, Oromia, Ethiopia

Received:2019-05-21 Revised:2020-11-27 Online:2022-12-30 Published:2022-11-09
Contact: Mukhdeep Singh Manshahia,Diriba Kajela Geleta E-mail:mukhdeep@gmail.com;kajeladiriba@yahoo.com

摘要/Abstract

Abstract: In this paper, a hybrid of grey wolf optimization (GWO) and genetic algorithm (GA) has been implemented to minimize the annual cost of hybrid of wind and solar renewable energy system. It was named as hybrid of grey wolf optimization and genetic algorithm (HGWOGA). HGWOGA was applied to this hybrid problem through three procedures.First,thebalancebetweentheexplorationandtheexploitationprocesswas done by grey wolf optimizer algorithm. Then, we divided the population into subpopulation and used the arithmetical crossover operator to utilize the dimension reduction and the population partitioning processes. At last, mutation operator was applied in the whole population in order to refrain from the premature convergence and trapping in local minima. MATLAB code was designed to implement the proposed methodology. The result of this algorithm is compared with the results of iteration method, GWO, GA, artificial bee colony (ABC) and particle swarm optimization (PSO) techniques. The results obtained by this algorithm are better when compared with those mentioned in the text.

Key words: Hybrid renewable energy, Optimization, Nature-inspired algorithm, Grey wolf optimization, Genetic algorithm

中图分类号:

. [J]. Journal of the Operations Research Society of China, 2022, 10(4): 749-762.

Diriba Kajela Geleta, Mukhdeep Singh Manshahia. A Hybrid of Grey Wolf Optimization and Genetic Algorithm for Optimization of Hybrid Wind and Solar Renewable Energy System[J]. Journal of the Operations Research Society of China, 2022, 10(4): 749-762.

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A Hybrid of Grey Wolf Optimization and Genetic Algorithm for Optimization of Hybrid Wind and Solar Renewable Energy System

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